Showing papers in "IEEE Transactions on Industrial Electronics in 2020"

Position Sensorless Permanent Magnet Synchronous Machine Drives—A Review

Abstract: Owing to the competitive advantages of cost reduction, system downsizing, and reliability enhancement, position sensorless control methods for permanent magnet synchronous machine drives have drawn increasing attention from academia to industrial applications. In this article, a survey of the major sensorless control techniques for a wide speed range from low to high speeds is presented. The different high frequency signal injection schemes, fundamental pulsewidth modulation excitation methods, and model-based sensorless control are displayed and compared, which is able to facilitate the sensorless control implementation.

A Decentralized Bilateral Energy Trading System for Peer-to-Peer Electricity Markets

Abstract: Increase in the deployment of distributed energy resources (DERs) has triggered a new trend to redesign electricity markets as consumer-centric markets relying on peer-to-peer (P2P) approaches. In the P2P markets, players can directly negotiate under bilateral energy trading to match demand and supply. The trading scheme should be designed adequately to incentivise players to participate in the trading process actively. This article proposes a decentralized P2P energy trading scheme for electricity markets with high penetration of DERs. A novel algorithm using primal-dual gradient method is described to clear the market in a fully decentralized manner without interaction of any central entity. Also, to incorporate technical constraints in the energy trading, line flow constraints are modeled in the bilateral energy trading to avoid overloaded or congested lines in the system. This market structure respects market players’ preferences by allowing bilateral energy trading with product differentiation. The performance of the proposed method is evaluated using simulation studies, and it is found that market players can trade energy to maximize their welfare without violating line flow constraints. Also, compared with other similar methods for P2P trading, the proposed approach needs lower data exchange and has a faster convergence.

A Motion Planning and Tracking Framework for Autonomous Vehicles Based on Artificial Potential Field Elaborated Resistance Network Approach

Abstract: This paper presents a novel motion planning and tracking framework for automated vehicles based on artificial potential field (APF) elaborated resistance approach. Motion planning is one of the key parts of autonomous driving, which plans a sequence of movement states to help vehicles drive safely, comfortably, economically, human-like, etc. In this paper, the APF method is used to assign different potential functions to different obstacles and road boundaries; while the drivable area is meshed and assigned resistance values in each edge based on the potential functions. A local current comparison method is employed to find a collision-free path. As opposed to a path, the vehicle motion or trajectory should be planned spatiotemporally. Therefore, the entire planning process is divided into two spaces, namely the virtual and actual. In the virtual space, the vehicle trajectory is predicted and executed step by step over a short horizon with the current vehicle speed. Then, the predicted trajectory is evaluated to decide if the speed should be kept or changed. Finally, it will be sent to the actual space, where an experimentally validated Carsim model controlled by a model predictive controller is used to track the planned trajectory. Several case studies are presented to demonstrate the effectiveness of the proposed framework.

Disturbance Observer-Based Robust Control and Its Applications: 35th Anniversary Overview

Abstract: Disturbance observer (DOB) has been one of the most widely used robust control tools since it was proposed by Ohnishi in 1983. This paper introduces the origins of DOB and presents a survey of the major results on DOB-based robust control in the last 35 years. Furthermore, it explains DOB's analysis and synthesis techniques for linear and nonlinear systems by using a unified framework. In final section, this paper presents concluding remarks on DOB-based robust control and its engineering applications.

An Improved Delay-Suppressed Sliding-Mode Observer for Sensorless Vector-Controlled PMSM

Abstract: This article presents a delay-suppressed sliding-mode observer (SMO) to observe the real-time rotor position of a permanent magnet synchronous machine (PMSM) controlled by vector control algorithms. First, in order to solve the low-pass filter (LPF) delay problem existing in the traditional signum function-based SMO, a brand new hyperbolic function is initially selected as the switching function. Because a hyperbolic function with a proper boundary layer is capable of reducing the chattering phenomenon of an SMO, it is not necessary to reemploy LPFs to eliminate the adverse impacts of chattering on the position estimation accuracy. In order to ensure the reachability and stability of the hyperbolic-function-based SMO, the observer gain is calculated by the means of a Lyapunov function in this article. Second, to solve the problem of calculation delay caused by digital computation, a current precompensation scheme based on dual-sampling strategy in one switching period is proposed. After compensating the calculation delay, the accuracy of position estimation as well as the motor control performance can be improved. Finally, the proposed SMOs with and without delay compensation are verified by both simulation and experiments that are conducted on a three-phase 1.5-kW PMSM drive prototype.

Disturbance Observer Design for Nonlinear Systems Represented by Input–Output Models

Abstract: A new approach to the design of nonlinear disturbance observers (DOBs) for a class of nonlinear systems described by input–output differential equations is presented in this paper. In contrast with established forms of nonlinear DOBs, the most important feature of this new type of DOB is that only measurement of the output variable is required, rather than the state variables. An inverse simulation model is first constructed based on knowledge of the structure and parameters of a conventional model of the system. The disturbance can then be estimated by comparing the output of the inverse model and the input of the original nonlinear system. Mathematical analysis demonstrates the convergence of this new form of nonlinear DOB. The approach has been applied to disturbance estimation for a linear system and a new form of linear DOB has been developed. The differences between the proposed linear DOB and the conventional form of frequency-domain DOB are discussed through a numerical example. Finally, the nonlinear DOB design method is illustrated through an application involving a simulation of a jacketed continuous stirred tank reactor system.

SDDNet: Real-Time Crack Segmentation

Abstract: This article reports the development of a pure deep learning method for segmenting concrete cracks in images. The objectives are to achieve the real-time performance while effectively negating a wide range of various complex backgrounds and crack-like features. To achieve the goals, an original convolutional neural network is proposed. The model consists of standard convolutions, densely connected separable convolution modules, a modified atrous spatial pyramid pooling module, and a decoder module. The semantic damage detection network (SDDNet) is trained on a manually created crack dataset, and the trained network records the mean intersection-over-union of 0.846 on the test set. Each test image is analyzed, and the representative segmentation results are presented. The results show that the SDDNet segments cracks effectively unless the features are too faint. The proposed model is also compared with the most recent models, which show that it returns better evaluation metrics even though its number of parameters is 88 times less than in the compared models. In addition, the model processes in real-time (36 FPS) images at 1025 × 512 pixels, which is 46 times faster than in a recent work.

Broad Convolutional Neural Network Based Industrial Process Fault Diagnosis With Incremental Learning Capability

Abstract: Fault diagnosis, which identifies the root cause of the observed out-of-control status, is essential to counteracting or eliminating faults in industrial processes. Many conventional data-driven fault diagnosis methods ignore the fault tendency of abnormal samples, and they need a complete retraining process to include the newly collected abnormal samples or fault classes. In this article, a broad convolutional neural network (BCNN) is designed with incremental learning capability for solving the aforementioned issues. The proposed method combines several consecutive samples as a data matrix, and it then extracts both fault tendency and nonlinear structure from the obtained data matrix by using convolutional operation. After that, the weights in fully connected layers can be trained based on the obtained features and their corresponding fault labels. Because of the architecture of this network, the diagnosis performance of the BCNN model can be improved by adding newly generated additional features. Finally, the incremental learning capability of the proposed method is also designed, so that the BCNN model can update itself to include new coming abnormal samples and fault classes. The proposed method is applied both to a simulated process and a real industrial process. Experimental results illustrate that it can better capture the characteristics of the fault process, and effectively update diagnosis model to include new coming abnormal samples, and fault classes.

Adaptive Fuzzy Backstepping Control for Stable Nonlinear Bilateral Teleoperation Manipulators With Enhanced Transparency Performance

Abstract: Bilateral teleoperation technology has been widely concerned by its unique advantages in human–machine interaction-based cooperative operation systems. Communication delay, various nonlinearities, and uncertainties in teleoperation system are the main challenging issues to achieve system stability and good transparency performance. In this paper, a globally stable adaptive fuzzy backstepping control design is proposed for nonlinear bilateral teleoperation manipulators to handle the aforementioned issues. For the communication channel, instead of direct transmission of environmental torque signals, the fuzzy-based nonpower approximate environmental parameters are transmitted to the master side for environmental torque prediction, which effectively avoids the transmission of power signals in the delayed communication channel and solves the passivity problem in the traditional teleoperation system. A trajectory generator is implemented in the master side and a trajectory smoothing is applied in the slave side. Subsequently, nonlinear adaptive fuzzy backstepping controllers for master and slave are separately designed to handle the nonlinearities and uncertainties. Theoretically, the great transparency performance of both position tracking and force feedback can be achieved, and the global stability is still guaranteed under communication delay. Comparative experiments are conducted on the real platform, which verify the effectiveness and advantages of the proposed control design in some typical working scenarios.

Transfer Learning With Long Short-Term Memory Network for State-of-Health Prediction of Lithium-Ion Batteries

Abstract: Existing state-of-health (SOH) data-driven prediction techniques for lithium-ion batteries are subject to mass training data, which leads to limited application. To face the challenge, in this article, we propose a novel SOH prediction method based on transfer learning. The long short-term memory (LSTM) combined with fully connected (FC) layers is designed as the base model. The LSTM can learn the long-term dependencies of battery aging to reduce the noise sensitivity of the prediction model, and the FC layers serve as the “firewall” during the transferring process. A feature expression scoring (FES) rule is developed to assess the relevance of multiple prediction tasks. Different from traditional transfer learning, we select the task with the highest FES score to obtain the base model with superior generalization performance. During transfer learning, the fine-tuning strategy is executed for the tasks with high scores, but rebuilding strategy for the low score one. Only using the first 25% of a dataset for transfer training, our technique can predict more phases compared to traditional data-driven methods, which will avoid more unreasonable operations from users. The experimental results verify that the proposed method can achieve accurate, fast, and steady SOH prediction. Compared to some existing data-driven methods, our method obtains optimal performance.

Flux–Charge Analysis of Two-Memristor-Based Chua' s Circuit: Dimensionality Decreasing Model for Detecting Extreme Multistability

Abstract: In this paper, from a new perspective of flux and charge, we present in-depth analyses of two ideal memristor emulators and the fifth-order memristive Chua's circuit constructed based on them. The constitutive flux–charge relations of the two adopted memristor emulators are first formulated, and their initial-dependent characteristics are numerically revealed and experimentally verified. Thereafter, with these two constitutive relations, a third-order dimensionality decreasing flux–charge model for the fifth-order memristive Chua's circuit is constructed, in which five extra constant system parameters are introduced to indicate the initial states of the five dynamic elements. Numerical simulations confirm that this newly constructed model possesses several determined equilibria and maintains the initial-dependent dynamics of the original voltage–current model. Thus, the complex and sensitive initial state-related extreme multistability phenomenon can be deeply explored through theoretical analyses and hardware measurements. It is demonstrated that the sensitive extreme multistability phenomenon becomes detectable in the flux–charge domain, which is efficient for exploring the inner mechanisms and further seeking possible applications of this special phenomenon.

Nonlinear Control of Underactuated Systems Subject to Both Actuated and Unactuated State Constraints With Experimental Verification

Abstract: In practice, underactuated systems are widely used, and their control problems have attracted much attention in recent years. For safety concerns or transient performance requirements, some state constraints should be guaranteed both theoretically and practically, particularly those exerted on unactuated states. To the best of our knowledge, there are few closed-loop control methods that can treat unactuated state constraints with theoretical guarantees (i.e., that can theoretically guarantee unactuated state variables to be always within the preset ranges). For this purpose, in this article, we propose a new control strategy for a class of underactuated systems that can treat the various constraints including actuated and unactuated state constraints and the constraints on some specific composite variables . Specifically, we elaborately design some new auxiliary terms that are composed of constrained variable signals and actuated velocity signals. These terms can enhance the couplings between unactuated and actuated states that are further utilized to tackle the unactuated state and composite variable constraints. Then, the performance of the designed method is proven by rigorous analysis. Finally, the proposed method is applied to a double pendulum crane system and a tower crane system to verify its superior performance by experiments.

Deep Learning-Based Machinery Fault Diagnostics With Domain Adaptation Across Sensors at Different Places

Abstract: In the recent years, data-driven machinery fault diagnostic methods have been successfully developed, and the tasks where the training and testing data are from the same distribution have been well addressed. However, due to sensor malfunctions, the training and testing data can be collected at different places of machines, resulting in the feature space with significant distribution discrepancy. This challenging issue has received less attention in the current literature, and the existing approaches generally fail in such scenarios. This article proposes a domain adaptation method for machinery fault diagnostics based on deep learning. Adversarial training is introduced for marginal domain fusion, and unsupervised parallel data are explored to achieve conditional distribution alignments with respect to different machine health conditions. Experiments on two rotating machinery datasets are carried out for validations. The results suggest the proposed method is promising to address the fault diagnostic tasks with data from different places of machines, further enhancing applicability of data-driven methods in real industries.

$H_\infty$ Refined Antidisturbance Control of Switched LPV Systems With Application to Aero-Engine

Abstract: In this paper, a parameter-dependent multiple discontinuous Lyapunov function (PMDLF) approach is proposed to study the $H_\infty$ refined antidisturbance control problem of switched linear parameter-varying systems. The $H_\infty$ refined antidisturbance means the disturbance appearing in the control channel can be accurately compensated by means of the estimation of the disturbance and the energy bounded external disturbance can be restrained. A key point is to set up a PMDLF framework that provides an effective tool for attenuating the energy bounded disturbances and rejecting the disturbances generated by the exosystem accurately. A parameter-driven and dwell time-dependent switching law is designed, and a solvability condition ensuring the $H_\infty$ refined antidisturbance performance is developed. Then, the $H_\infty$ refined antidisturbance switched parameter-dependent disturbance observers and the disturbance observer-based refined controllers are established to achieve required disturbance attenuation and rejection. Finally, an example of an aero-engine control system is given to verify the availability of the acquired approaches.

Bayesian Deep-Learning-Based Health Prognostics Toward Prognostics Uncertainty

Abstract: Deep-learning-based health prognostics is receiving ever-increasing attention. Most existing methods leverage advanced neural networks for prognostics performance improvement, providing mainly point estimates as prognostics results without addressing prognostics uncertainty. However, uncertainty is critical for both health prognostics and subsequent decision making, especially for safety-critical applications. Inspired by the idea of Bayesian machine learning, a Bayesian deep-learning-based (BDL-based) method is proposed in this paper for health prognostics with uncertainty quantification. State-of-the-art deep learning models are extended into Bayesian neural networks (BNNs), and a variational-inference-based method is presented for the BNNs learning and inference. The proposed method is validated through a ball bearing dataset and a turbofan engine dataset. Other than point estimates, health prognostics using the BDL-based method is enhanced with uncertainty quantification. Scalability and generalization ability of state-of-the-art deep learning models can be well inherited. Stochastic regularization techniques, widely available in mainstream software libraries, can be leveraged to efficiently implement the BDL-based method for practical applications.

Force Sensorless Admittance Control With Neural Learning for Robots With Actuator Saturation

Abstract: In this paper, we present a sensorless admittance control scheme for robotic manipulators to interact with unknown environments in the presence of actuator saturation. The external environment is defined as linear models with unknown dynamics. Using admittance control, the robotic manipulator is controlled to be compliant with external torque from the environment. The external torque acted on the end-effector is estimated by using a disturbance observer based on generalized momentum. The model uncertainties are solved by using radial basis neural networks (NNs). To guarantee the tracking performance and tackle the effect of actuator saturation, an adaptive NN controller integrating an auxiliary system is designed to handle the actuator saturation. By employing Lyapunov stability theory, the stability of the closed-loop system is achieved. The experiments on the Baxter robot are implemented to verify the effectiveness of the proposed method.

Deep Learning Approaches on Pedestrian Detection in Hazy Weather

Abstract: Effectively detecting pedestrians in various environments would significantly improve driving safety for autonomous vehicles. However, the degrpted visibility and blurred outline and appearance of pedestrian images captured during hazy weather strongly limit the effectiveness of current pedestrian detection methods. To solve this problem, this article presents three novel deep learning approaches based on you only look once. The depth wise separable convolution and linear bottleneck skills were used to reduce the computational cost and number of parameters, rendering our network more efficient. We also innovatively developed a weighted combination layer in one of the approaches by combining multiscale feature maps and a squeeze and excitation block. Collected pedestrian images in hazy weather were augmented using six strategies to enrich the database. Experimental results show that our proposed methods can effectively detect pedestrians in hazy weather, significantly outperforming state-of-the-art methods in both accuracy and speed.

A Polynomial Kernel Induced Distance Metric to Improve Deep Transfer Learning for Fault Diagnosis of Machines

Abstract: Deep transfer-learning-based diagnosis models are promising to apply diagnosis knowledge across related machines, but from which the collected data follow different distribution. To reduce the distribution discrepancy, Gaussian kernel induced maximum mean discrepancy (GK-MMD) is a widely used distance metric to impose constraints on the training of diagnosis models. However, the models using GK-MMD have three weaknesses: 1) GK-MMD may not accurately estimate distribution discrepancy because it ignores the high-order moment distances of data; 2) the time complexity of GK-MMD is high to require much computation cost; 3) the transfer performance of GK-MMD-based diagnosis models is sensitive to the selected kernel parameters. In order to overcome the weaknesses, a distance metric named polynomial kernel induced MMD (PK-MMD) is proposed in this article. Combined with PK-MMD, a diagnosis model is constructed to reuse diagnosis knowledge from one machine to the other. The proposed methods are verified by two transfer learning cases, in which the health states of locomotive bearings are identified with the help of data respectively from motor bearings and gearbox bearings in laboratories. The results show that PK-MMD enables to improve the inefficient computation of GK-MMD, and the PK-MMD-based diagnosis model presents better transfer results than other methods.

Macroscopic–Microscopic Attention in LSTM Networks Based on Fusion Features for Gear Remaining Life Prediction

Abstract: In the mechanical transmission system, the gear is one of the most widely used transmission components. The failure of the gear will cause serious accidents and huge economic loss. Therefore, the remaining life prediction of the gear is of great importance. In order to accurately predict the remaining life of the gear, a new type of long-short-term memory neural network with macroscopic–microscopic attention (MMA) is proposed in this article. First, some typical time-domain and frequency-domain characteristics of vibration signals are calculated, respectively, such as the maximum value, the absolute mean value, the standard deviation, the kurtosis, and so on. Then, the principal component of these characteristics is extracted by the isometric mapping method. The importance of fusional characteristic information is filtered via a proposed MMA mechanism so that the input weight of neural network data and recursive data can reach multilevel real-time amplification. With the new long short-term memory neural network, the health characteristics of gear vibration signals can be predicted based on the known fusion features. The experimental results show that this method can predict the remaining life of gears and bearings well, and it has higher prediction accuracy than the conventional prediction methods.

Remaining Useful Life Estimation of Structure Systems Under the Influence of Multiple Causes: Subsea Pipelines as a Case Study

Abstract: In dynamic complex environments, the degradation of structure systems is generally caused not by a single factor but by multiple ones, and the process is subject to a high level of uncertainty. This article contributes a hybrid physics-model-based and data-driven remaining useful life (RUL) estimation methodology of structure systems considering the influence of multiple causes by using dynamic Bayesian networks (DBNs). The structure model and parameter model of DBNs for the degradation process caused by a single factor are established on the basis of theoretical or empirical physical models, thereby solving the problem of insufficient data. An RUL estimation model is subsequently established by integrating these degradation process models. The RUL value is obtained from the time difference between the detection point and predicted failure point, which is determined using the failure threshold of performance. The sensor data and expert knowledge can be input into the estimation model to update the RUL value whenever necessary. The subsea pipelines in offshore oil and gas subsea production systems are adopted to demonstrate the proposed methodology. The degradation processes with fatigue, corrosion, sand erosion, and internal waves are modeled using DBNs, and the RUL is estimated using a DBN-based RUL methodology.

Online Fault Diagnosis of External Short Circuit for Lithium-Ion Battery Pack

Abstract: Battery safety is one of the most crucial issues in the utilization of lithium-ion batteries (LiBs) for all-climate electric vehicles. Short circuit, overcharge, and overheat are three common field failures of LiBs. In this paper, online fault diagnosis for external short circuit (ESC) of LiB packs is investigated. The experiments are carried out to obtain and compare ESC characteristics of 18650-type NMC battery pack and single cell. Based on the analysis of experimental results, a two-step equivalent circuit model is established to describe the ESC process and an online model-based scheme is proposed to diagnose ESC faults of battery packs. The proposed scheme is evaluated by experimental data. The results show that it can effectively diagnose ESC faults in 3.5 s after their occurrences with the terminal voltage error less than 25 mV. The proposed scheme has shown great generalization ability. ESC faults of battery packs under different number of cells connected in series and unavailable current information can also be diagnosed at the terminal voltage error less than 48 and 60 mV, respectively.

High-Order Disturbance-Observer-Based Sliding Mode Control for Mobile Wheeled Inverted Pendulum Systems

Abstract: In this paper, a novel high-order disturbance observer (HODO) for the mobile wheeled inverted pendulum (MWIP) system is first proposed. Based on a choice method of optimal gain matrices, the estimation accuracy of the HODO can be improved. Combining the proposed HODO and sliding mode control (SMC), a new control strategy is designed for the balance and speed control of the MWIP system. The boundness of the estimation error of HODO is proved and the stability of the closed-loop control system is achieved through the appropriate selection of sliding surface coefficients. The effectiveness of all proposed methods is verified by experiments on a real MWIP system.

An Approach Towards Extreme Fast Charging Station Power Delivery for Electric Vehicles with Partial Power Processing

Abstract: This article proposes an approach for realizing the power delivery scheme for an extreme fast charging (XFC) station that is meant to simultaneously charge multiple electric vehicles (EVs). A cascaded H-bridge converter is utilized to directly interface with the medium voltage grid while dual-active-bridge based soft-switched solid-state transformers are used to achieve galvanic isolation. The proposed approach eliminates redundant power conversion by making use of partial power rated dc–dc converters to charge the individual EVs. Partial power processing enables independent charging control over each EV, while processing only a fraction of the total battery charging power. Practical implementation schemes for the partial power charger unit are analyzed. A phase-shifted full-bridge converter-based charger is proposed. Design and control considerations for enabling multiple charging points are elucidated. Experimental results from a down-scaled laboratory test-bed are provided to validate the control aspects, functionality, and effectiveness of the proposed XFC station power delivery scheme. With a down-scaled partial power converter that is rated to handle only 27% of the battery power, an efficiency improvement of 0.6% at full-load and 1.6% at 50% load is demonstrated.

Tuning of Digital PID Controllers Using Particle Swarm Optimization Algorithm for a CAN-Based DC Motor Subject to Stochastic Delays

Abstract: In this article, we investigate the tuning problem of digital proportional-integral-derivative (PID) parameters for a dc motor controlled via the controller area network (CAN). First, the model of the dc motor is presented with its parameters being identified with experimental data. By studying the CAN network characteristics, we obtain the CAN-induced delays related to the load rate and the priorities. Then, considering the system model, the network properties, and the digital PID controller, the tuning problem of PID parameters for the CAN-based dc motor is transformed into a design problem of a static-output-feedback controller for a time-delayed system. To solve this problem, particle swarm optimization algorithm and linear-quadratic-regulator method are adopted by incorporating the sufficient condition of time-varying delay system. Finally, the effectiveness of the proposed PID tuning strategy is validated by experimental results.

Data-Driven Fault Diagnosis Method Based on Compressed Sensing and Improved Multiscale Network

Abstract: The diagnosis of the key components of rotating machinery systems is essential for the production efficiency and quality of manufacturing processes. The performance of the traditional diagnosis method depends heavily on feature extraction, which relies on the degree of individual's expertise or prior knowledge. Recently, a deep learning (DL) method is applied to automate feature extraction. However, training in the DL method requires a massive amount of sensor data, which is time consuming and poses a challenge for its applications in engineering. In this paper, a new data-driven fault diagnosis method based on compressed sensing (CS) and improved multiscale network (IMSN) is proposed to recognize and classify the faults in rotating machinery. CS is used to reduce the amount of raw data, from which the fault information is discovered. At the same time, it can be used to generate sufficient training samples for the subsequent learning. The one-dimensional compressed signal is converted to two-dimensional image for further learning. An IMSN is established for learning and obtaining deep features. It improves the diagnosis performance of the DL process. The faults of the key components are identified from a softmax model. Experimental analysis is performed to verify effectiveness of the proposed data-driven fault diagnosis method.

Event-Triggered Adaptive Attitude Tracking Control for Spacecraft With Unknown Actuator Faults

Abstract: This paper is devoted to attitude tracking control of fractionated spacecraft with wireless communication. We consider the practical case that the spacecraft suffers from uncertain inertia parameters, external disturbances, and even unknown and time-varying actuator faults. Within the framework of the backstepping method, a novel event-triggered adaptive fault-tolerant control scheme is proposed. In our design, an event-triggering mechanism is introduced to determine the time instants for communication, which successfully avoids continuous communication and Zeno phenomenon. Then, with the aid of a bound estimation approach and a smooth function, the impacts of the actuator faults, as well as the network-induced error, are effectively compensated for. Moreover, by employing the prescribed performance control technique, it is shown that the attitude tracking errors can converge to predefined arbitrarily small residual sets with prescribed convergence rate and maximum overshoot, no matter if there exist unknown actuator faults. Compared with conventional adaptive attitude control schemes, the proposed scheme significantly reduces the communication burden, while providing high reliability and stable, rapid, and accurate response for attitude maneuvers. Simulation results are presented to illustrate the effectiveness of the proposed scheme.

Boundary Antidisturbance Control of a Spatially Nonlinear Flexible String System

Abstract: The aim of this article is to establish a novel boundary antidisturbance control to dampen the oscillation of a spatially nonlinear flexible string system influenced by unknown disturbances. To realize this, new disturbance rejection controllers are constructed to effectively suppress the external disturbances for vibration reduction with the help of a newly designed disturbance observer. Adopting Lyapunov direct method, we ensure that the derived control system is uniformly bounded stable. Simulations are also performed to demonstrate the feasibility and validity of the suggested strategy.

Event-Triggered Decentralized Tracking Control of Modular Reconfigurable Robots Through Adaptive Dynamic Programming

Abstract: This paper develops an event-triggered decentralized tracking control (DTC) approach for modular reconfigurable robots (MRRs) by using adaptive dynamic programming. By establishing a decentralized neural network (NN) observer, which uses local input–output data and desired states of coupling subsystems, the local dynamics of MRR subsystem can be obtained. In order to obtain the DTC, the tracking error subsystem is augmented by the exosystem with the desired trajectory. Based on the event-triggered mechanism and a modified local cost function, the DTC is derived by solving the local Hamilton–Jacobi–Bellman equation via a local critic NN with asymptotically stable structure. The stability of the entire closed-loop MRR system is analyzed by Lyapunov's direct method. The simulation of a two-degree of freedom MRR system ensures that the developed event-triggered DTC scheme is effective.

Adaptive Neural Network Control of Underactuated Surface Vessels With Guaranteed Transient Performance: Theory and Experimental Results

Abstract: In this paper, an adaptive trajectory tracking control algorithm for underactuated unmanned surface vessels (USVs) with guaranteed transient performance is proposed. To meet the realistic dynamical model of USVs, we consider that the mass and damping matrices are not diagonal and the input saturation problem. Neural networks (NNs) are employed to approximate the unknown external disturbances and uncertain hydrodynamics of USVs. Moreover, both full-state feedback control and output feedback control are presented, and the unmeasurable velocities of the output feedback controller are estimated via high-gain observer. Unlike the conventional control methods, we employ the error transformation function to guarantee the transient tracking performance. Both simulation and experimental results are carried out to validate the superior performance via comparing with traditional potential integral control approaches.

A 300-kHz 6.6-kW SiC Bidirectional LLC Onboard Charger

Abstract: The reverse LLC voltage gain is lower than unity. The dc-link voltage is then lower than the peak grid voltage so that the buck type dc–ac converters cannot be grid-tied directly, which causes the LLC reverse operation difficult in bidirectional application. This paper proposes a silicon carbide (SiC) bidirectional LLC charger architecture to achieve high efficiency and high power density. The first stage is an interleaved bridgeless totem pole power factor correction (PFC) to achieve unity power factor. The second stage is a 300-kHz LLC taking advantage of wide zero voltage switching (ZVS) range and magnetic integration. Thanks to extra control freedom of high dc-link voltage, the dc–dc voltage gain regulation is shared by the dc–ac stage taking advantage of high voltage SiC MOSFETs. A reverse LLC voltage gain compensation control by regulating dc-link voltage is proposed to enable the LLC bidirectional operation. A digital adaptive synchronous rectification driving scheme is proposed based on the LLC primary driver signals, which immunes to the circuit oscillation caused by high dv/dt and parasitic elements. A 6.6 kW SiC bidirectional LLC charger is build. The power density is 3.42 kW/L with 3 kW/kg and increases 55.6% over the reference design. The charging efficiency is above 96% through the battery voltage from 240 to 420 V, and 2% higher than the state-of-the-art products. The peak discharging efficiency under 6.6 kW is 96%, and 1% higher than the state-of-the-art efficiency.